Remote Data Scientist Jobs in Canada
82 Data Scientist jobs in Canada available for remote work from home. Apply for positions such as DATA SCIENTIST, Data Scientist, Data Scientist and more! Discover the best work-from-home or hybrid, full- and part-time jobs.
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The Data Scientist will collect, clean, and analyze structured and unstructured data to solve business problems. They will also assist in building machine learning models and creating data visualizations to support product development.
Evaluate and red-team AI-generated data analyses to ensure statistical rigor and sound methodology. Build, train, and evaluate machine learning models while documenting assumptions and collaborating with cross-functional teams.
The Data Scientist will research and develop algorithms to detect bots and fight fraud while partnering with cross-functional teams to optimize ML infrastructure. They will also contribute to software tooling and automation to improve detection capabilities and maintain codebase health.
You will partner with cross-functional teams to turn ambiguous business questions into rigorous analyses, models, and experiments. You will own the end-to-end process from framing questions to landing actionable recommendations with leadership.
Evaluate AI model outputs across statistics, machine learning, and quantitative reasoning to ensure high-quality performance. Create expert-level prompts, datasets, and reference solutions while providing structured feedback to improve model capabilities.
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The Data Scientist will develop and deploy predictive algorithms and production pipelines to analyze automotive industry data. They will collaborate with engineering teams to optimize ETL processes and create visual reports to communicate findings to stakeholders.
You will design, develop, and deploy scalable machine learning models and robust data pipelines using the Databricks platform. Additionally, you will lead MLOps initiatives, ensure production reliability, and mentor team members on best practices in data science and AI integration.
Set cross-team data science priorities and lead the company KPI framework, including driver trees, sensitivity modeling, measurement standards, and reusable analytical automation. Establish governed self-service and agentic workflows, align Finance and Product on targets and trade-offs, and mentor scientists while driving adoption of trusted metrics.
You will lead a team of data scientists to architect and build predictive models, measurement frameworks, and data products for advertising. You will also partner with product and engineering teams to scale these systems from prototype to production while championing methodology to stakeholders.
You will design and interpret product experiments while developing predictive models to optimize user engagement and retention. You will also partner with cross-functional teams to define KPI frameworks and translate analytical findings into actionable product strategies.
You will own company-wide KPI frameworks and measurement workstreams to drive business outcomes and product strategy. This involves building reproducible analytical tools, modeling KPI drivers, and partnering cross-functionally to ensure data-driven decision-making.
You will serve as the senior analytical leader for the Play business, shaping strategy through advanced modeling, experimentation, and cross-functional collaboration. You will mentor team members and translate complex data into actionable recommendations for executive leadership.
You will build and deploy machine learning models to optimize lead generation, carrier matching, and media buying strategies. You will own the end-to-end lifecycle of these models, from data discovery and validation to production monitoring and performance tuning.
You will build and deploy production-grade machine learning models to optimize ad bidding and carrier conversion for consumer brands. This role involves end-to-end ownership of data pipelines, model validation, and performance monitoring in a fast-paced environment.
Design, develop, and optimize advanced Pricing and Revenue Growth Management models to support strategic commercial decisions. Collaborate with cross-functional teams to translate complex business challenges into statistically robust predictive models.
You will own the scientific and product roadmap for the Monetization domain, leading the development of audience creation and measurement products. This involves collaborating with cross-functional teams to turn data signals into actionable insights and scalable commercial products.
You will independently own a defined audience or measurement product, managing the full lifecycle from problem framing to delivery. You will partner with cross-functional teams to translate advertiser goals into predictive models and actionable datasets.
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The Staff Data Scientist will define and own the company-wide measurement framework, causal strategy, and semantic data architecture. They will lead scientific governance, oversee strategic modeling, and raise the technical maturity of the organization through mentorship and best practices.
You will own the data science strategy and roadmap for ranking and relevance, partnering with engineering and product teams to optimize marketplace outcomes. This involves designing experiments, building measurement frameworks, and translating complex business goals into actionable ranking improvements.
Develop, deploy, and maintain predictive models for LTV, churn, and retention while owning the modeling roadmap. Partner with data engineering to productionize models and build AI-powered analytics tools to inform business decisions.
Design and deploy real-time courier pay and boost algorithms to balance supply with delivery demand. Lead causal inference and experimentation frameworks to measure incremental impact while collaborating with product and engineering teams.
Design and deploy real-time courier pay and boost algorithms to balance supply with delivery demand. Lead causal inference and experimentation frameworks to measure incremental impact while collaborating with product and engineering teams.
The Associate Data Scientist will assist with client engagements by measuring data de-identification and re-identification risks. Responsibilities include preparing client datasets, validating data, and supporting the delivery of privacy solutions through reports and remote coaching.
The role involves designing, implementing, and validating algorithmic components within a consulting project team. You will also develop services and APIs to integrate applications with algorithms while solving mathematical optimization and operations research problems.
The Senior Data Scientist will own a portfolio of customer deployments, proactively identifying performance issues and diagnosing system failures. They will also encode operational expertise into AI logic, validate model performance, and collaborate with cross-functional teams to drive impact.
You will partner with cross-functional leaders to shape the growth strategy, size opportunities, and prioritize the product roadmap. Additionally, you will own the growth funnel metrics and design experiments to drive customer acquisition, activation, and retention.
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You will lead marketing measurement efforts by designing incrementality tests and applying causal inference methods to optimize marketing spend across various channels. Additionally, you will build attribution models and provide actionable budget allocation recommendations to marketing and finance leadership.
Architect and develop modular agentic AI applications while refactoring complex Python code for performance and scalability. Establish production-grade observability, testing, and operational controls to ensure reliable deployment of LLM-powered solutions.
The Principal Data Scientist will serve as the technical lead for an agentic AI program, overseeing architecture, evaluation standards, and delivery across multiple workstreams. They will partner with executive leadership to define the AI roadmap and ensure high-quality, scalable AI systems are deployed in production.
The role involves building complex machine learning and econometric models to solve unstructured business problems while ensuring data stability and production scalability. You will also lead data sourcing efforts, mentor less experienced team members, and collaborate with stakeholders to translate model outcomes into business value.
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